




"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''layoutlmv3'''







   def __init__(		self       ,					lowerCamelCase_=5_0_2_6_5       ,					lowerCamelCase_=7_6_8       ,					lowerCamelCase_=1_2       ,					lowerCamelCase_=1_2       ,					lowerCamelCase_=3_0_7_2       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=5_1_2       ,					lowerCamelCase_=2       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=1e-5       ,					lowerCamelCase_=1       ,					lowerCamelCase_=0       ,					lowerCamelCase_=2       ,					lowerCamelCase_=1_0_2_4       ,					lowerCamelCase_=1_2_8       ,					lowerCamelCase_=1_2_8       ,					lowerCamelCase_=True       ,					lowerCamelCase_=3_2       ,					lowerCamelCase_=1_2_8       ,					lowerCamelCase_=6_4       ,					lowerCamelCase_=2_5_6       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=2_2_4       ,					lowerCamelCase_=3       ,					lowerCamelCase_=1_6       ,					lowerCamelCase_=None       ,					**lowerCamelCase_       ,					)				->   Optional[Any]:
       super().__init__(
           vocab_size=lowerCamelCase_       ,					hidden_size=lowerCamelCase_       ,					num_hidden_layers=lowerCamelCase_       ,					num_attention_heads=lowerCamelCase_       ,					intermediate_size=lowerCamelCase_       ,					hidden_act=lowerCamelCase_       ,					hidden_dropout_prob=lowerCamelCase_       ,					attention_probs_dropout_prob=lowerCamelCase_       ,					max_position_embeddings=lowerCamelCase_       ,					type_vocab_size=lowerCamelCase_       ,					initializer_range=lowerCamelCase_       ,					layer_norm_eps=lowerCamelCase_       ,					pad_token_id=lowerCamelCase_       ,					bos_token_id=lowerCamelCase_       ,					eos_token_id=lowerCamelCase_       ,					**lowerCamelCase_       ,					)
       UpperCamelCase              =	max_ad_position_embeddings
       UpperCamelCase              =	coordinate_size
       UpperCamelCase              =	shape_size
       UpperCamelCase              =	has_relative_attention_bias
       UpperCamelCase              =	rel_pos_bins
       UpperCamelCase              =	max_rel_pos
       UpperCamelCase              =	has_spatial_attention_bias
       UpperCamelCase              =	rel_ad_pos_bins
       UpperCamelCase              =	max_rel_ad_pos
       UpperCamelCase              =	text_embed
       UpperCamelCase              =	visual_embed
       UpperCamelCase              =	input_size
       UpperCamelCase              =	num_channels
       UpperCamelCase              =	patch_size
       UpperCamelCase              =	classifier_dropout

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       version.parse('''1.12'''			)







   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       # The order of inputs is different for question answering and sequence classification
       if self.task in ["question-answering", "sequence-classification"]:
           return OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                   ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ])
       else:
           return OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                   ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
               ])







   @property
   def   UpperCAmelCase__			(		self)				->   float:
       return 1e-5







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return 1_2







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = -1       ,					lowerCamelCase_ = -1       ,					lowerCamelCase_ = False       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = 3       ,					lowerCamelCase_ = 4_0       ,					lowerCamelCase_ = 4_0       ,					)				->   Mapping[str, Any]:
       setattr(processor.image_processor       ,					'''apply_ocr'''       ,					lowerCamelCase_)

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCamelCase              =	compute_effective_axis_dimension(
           lowerCamelCase_       ,					fixed_dimension=OnnxConfig.default_fixed_batch       ,					num_token_to_add=0)
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase              =	processor.tokenizer.num_special_tokens_to_add(lowerCamelCase_)
       UpperCamelCase              =	compute_effective_axis_dimension(
           lowerCamelCase_       ,					fixed_dimension=OnnxConfig.default_fixed_sequence       ,					num_token_to_add=lowerCamelCase_)
       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase              =	[[''' '''.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       UpperCamelCase              =	[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
       UpperCamelCase              =	self._generate_dummy_images(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)

       UpperCamelCase              =	dict(
           processor(
               lowerCamelCase_       ,					text=lowerCamelCase_       ,					boxes=lowerCamelCase_       ,					return_tensors=lowerCamelCase_       ,					))

       return inputs



'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import CLIPSegProcessor, ViTImageProcessor





@require_vision
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= tempfile.mkdtemp()

											# fmt: off
											_SCREAMING_SNAKE_CASE					:			Any							= ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
											# fmt: on
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= dict(zip(__snake_case       ,   range(len(__snake_case    )    )    )    )
											_SCREAMING_SNAKE_CASE					:			str							= ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
											_SCREAMING_SNAKE_CASE					:			Any							= {"""unk_token""": """<unk>"""}

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["""vocab_file"""]    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["""merges_file"""]    )
											with open(self.vocab_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(__snake_case    ) + """\n"""    )
											with open(self.merges_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															fp.write("""\n""".join(__snake_case    )    )

											_SCREAMING_SNAKE_CASE					:			Any							= {
											    """do_resize""": True,
											    """size""": 20,
											    """do_center_crop""": True,
											    """crop_size""": 18,
											    """do_normalize""": True,
											    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
											    """image_std""": [0.26862954, 0.26130258, 0.27577711],
											}
											_SCREAMING_SNAKE_CASE					:			List[str]							= os.path.join(self.tmpdirname       ,   __snake_case    )
											with open(self.image_processor_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															json.dump(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return CLIPTokenizer.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return ViTImageProcessor.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self    ):
											shutil.rmtree(self.tmpdirname    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [np.random.randint(255       ,   size=(3, 30, 400)       ,   dtype=np.uinta    )]

											_SCREAMING_SNAKE_CASE					:			List[str]							= [Image.fromarray(np.moveaxis(__snake_case       ,   0       ,   -1    )    ) for x in image_inputs]

											return image_inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_tokenizer()
											_SCREAMING_SNAKE_CASE					:			int							= self.get_rust_tokenizer()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_image_processor()

											_SCREAMING_SNAKE_CASE					:			str							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )
											processor_slow.save_pretrained(self.tmpdirname    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= CLIPSegProcessor.from_pretrained(self.tmpdirname       ,   use_fast=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )
											processor_fast.save_pretrained(self.tmpdirname    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor.from_pretrained(self.tmpdirname    )

											self.assertEqual(processor_slow.tokenizer.get_vocab()       ,   tokenizer_slow.get_vocab()    )
											self.assertEqual(processor_fast.tokenizer.get_vocab()       ,   tokenizer_fast.get_vocab()    )
											self.assertEqual(tokenizer_slow.get_vocab()       ,   tokenizer_fast.get_vocab()    )
											self.assertIsInstance(processor_slow.tokenizer       ,   __snake_case    )
											self.assertIsInstance(processor_fast.tokenizer       ,   __snake_case    )

											self.assertEqual(processor_slow.image_processor.to_json_string()       ,   image_processor.to_json_string()    )
											self.assertEqual(processor_fast.image_processor.to_json_string()       ,   image_processor.to_json_string()    )
											self.assertIsInstance(processor_slow.image_processor       ,   __snake_case    )
											self.assertIsInstance(processor_fast.image_processor       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,   image_processor=self.get_image_processor()    )
											processor.save_pretrained(self.tmpdirname    )

											_SCREAMING_SNAKE_CASE					:			str							= self.get_tokenizer(bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_image_processor(do_normalize=__snake_case       ,   padding_value=1.0    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= CLIPSegProcessor.from_pretrained(
											    self.tmpdirname       ,   bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""       ,   do_normalize=__snake_case       ,   padding_value=1.0    )

											self.assertEqual(processor.tokenizer.get_vocab()       ,   tokenizer_add_kwargs.get_vocab()    )
											self.assertIsInstance(processor.tokenizer       ,   __snake_case    )

											self.assertEqual(processor.image_processor.to_json_string()       ,   image_processor_add_kwargs.to_json_string()    )
											self.assertIsInstance(processor.image_processor       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_processor(__snake_case       ,   return_tensors="""np"""    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= processor(images=__snake_case       ,   return_tensors="""np"""    )

											for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()       ,   input_processor[key].sum()       ,   delta=1e-2    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= """lower newer"""

											_SCREAMING_SNAKE_CASE					:			Any							= processor(text=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= tokenizer(__snake_case    )

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]       ,   encoded_processor[key]    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Dict							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= """lower newer"""
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[str]							= processor(text=__snake_case       ,   images=__snake_case    )

											self.assertListEqual(list(inputs.keys()    )       ,   ["""input_ids""", """attention_mask""", """pixel_values"""]    )

											# test if it raises when no input is passed
											with pytest.raises(__snake_case    ):
															processor()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= self.prepare_image_inputs()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= processor(images=__snake_case       ,   visual_prompt=__snake_case    )

											self.assertListEqual(list(inputs.keys()    )       ,   ["""pixel_values""", """conditional_pixel_values"""]    )

											# test if it raises when no input is passed
											with pytest.raises(__snake_case    ):
															processor()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_SCREAMING_SNAKE_CASE					:			int							= processor.batch_decode(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= tokenizer.batch_decode(__snake_case    )

											self.assertListEqual(__snake_case       ,   __snake_case    )




